Evidence map›Paper›PMID 36262424›Full record

ReviewNon-coding RNA research2023

A perspective to weaponize microRNAs against lung cancer.

Dhanashree Murugan, Loganathan Rangasamy

Abstract readReview
In one paragraph

Review in Non-coding RNA research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. May EPH/Ephrin Targeting Revolutionize Lung Cancer Treatment?International journal of molecular sciences · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Dhanashree MuruganDrug Discovery Unit (DDU), Centre for Biomaterials, Cellular, and Molecular Theranostics (CBCMT), Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India.
Loganathan RangasamyDrug Discovery Unit (DDU), Centre for Biomaterials, Cellular, and Molecular Theranostics (CBCMT), Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

microRNAs are regulatory RNAs that silence specific mRNA by binding to it, inducing translational repression. Over the recent decades since the discovery of RNA interference, the field of microRNA therapeutics has expanded tremendously. The role of miRNAs in disease development has attracted researchers to investigate their potential in therapeutics. In lung cancer, multiple miRNAs are deregulated, and their involvement is observed in cell proliferation, immunomodulation, angiogenesis, and epithelial-mesenchymal transition. Thus, synthetic oligonucleotides are developed to downregulate the overexpressed miRNA or to upregulate the repressed miRNA. However, their clinical efficiency is limited due to the requirement for an effective delivery strategy. Advances in the current understanding of nanotechnology, biomaterial science, and disease molecular pathology have increased the chances of overcoming the limitations of miRNA-based therapy. This review enlists downregulated and upregulated miRNAs in lung cancer. This review also highlights the major contributions to miRNA-based therapeutics for lung cancer and strategies to overcome endosomal barriers. It also attempts to understand the nuances between current advancements in delivery methods, advantages, disadvantages, and practical issues for the large-scale development of miRNA-based therapeutics.

Indexed as

AgomiRAntagomiRLung cancermiRNA-based therapeuticsRNA interference

Identifiers

PMID36262424
PMCPMC9556932

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.